FERRARI: an efficient framework for visual exploratory subgraph search in graph databases

Wang, Chaohui; Xie, Miao; Bhowmick, Sourav S.; Choi, Byron; Xiao, Xiaokui; Zhou, Shuigeng

doi:10.1007/s00778-020-00601-0

Cited by 5 publications

(1 citation statement)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, graph databases [22][23][24][25][26][27] have been spotlighted because large amounts of graph data such as social and biological networks have been generated because of technological advancement. Many graph DBMSs such Neo4j, ArangoDB, OrientDB, and Redis-Graph have been developed to store and query graph data [22].…”

Section: Related Workmentioning

confidence: 99%

GOMS: Large‐scale ontology management system using graph databases

Lee

Kang

2021

ETRI Journal

View full text Add to dashboard Cite

Large-scale ontology management is one of the main issues when using ontology data practically. Although many approaches have been proposed in relational database management systems (RDBMSs) or object-oriented DBMSs (OODBMSs) to develop large-scale ontology management systems, they have several limitations because ontology data structures are intrinsically different from traditional data structures in RDBMSs or OODBMSs. In addition, users have difficulty using ontology data because many terminologies (ontology nodes) in large-scale ontology data match with a given string keyword. Therefore, in this study, we propose a (graph database-based ontology management system (GOMS) to efficiently manage large-scale ontology data. GOMS uses a graph DBMS and provides new query templates to help users find key concepts or instances. Furthermore, to run queries with multiple joins and path conditions efficiently, we propose GOMS encoding as a filtering tool and develop hash-based join processing algorithms in the graph DBMS. Finally, we experimentally show that GOMS can process various types of queries efficiently.

show abstract

Section: Related Workmentioning

confidence: 99%

GOMS: Large‐scale ontology management system using graph databases

Lee

Kang

2021

ETRI Journal

View full text Add to dashboard Cite

show abstract

FLAG: Towards Graph Query Autocompletion for Large Graphs

Choi

et al. 2022

Data Sci. Eng.

View full text Add to dashboard Cite

Graph query autocompletion (GQAC) takes a user’s graph query as input and generates top-k query suggestions as output, to help alleviate the verbose and error-prone graph query formulation process in a visual interface. To compose a target query with GQAC, the user may iteratively adopt suggestions or manually add edges to augment the existing query. The current state-of-the-art of GQAC, however, focuses on a large collection of small- or medium-sized graphs only. The subgraph features exploited by existing GQAC are either too small or too scarce in large graphs. In this paper, we present Flexible graph query autocompletion for LArge Graphs, called FLAG. We are the first to propose wildcard labels in the context of GQAC, which summarizes query structures that have different labels. FLAG allows augmenting users’ queries with subgraph increments with wildcard labels to form suggestions. To support wildcard-enabled suggestions, a new suggestion ranking function is proposed. We propose an efficient ranking algorithm and extend an index to further optimize the online suggestion ranking. We have conducted a user study and a set of large-scale simulations to verify both the effectiveness and efficiency of FLAG. The results show that the query suggestions saved roughly 50% of mouse clicks and FLAG returns suggestions in few seconds.

show abstract

Mining subgraph coverage patterns from graph transactions

Reddy

Mondal

2021

Int J Data Sci Anal

View full text Add to dashboard Cite

Pattern mining from graph transactional data (GTD) is an active area of research with applications in the domains of bioinformatics, chemical informatics and social networks. Existing works address the problem of mining frequent subgraphs from GTD. However, the knowledge concerning the coverage aspect of a set of subgraphs is also valuable for improving the performance of several applications. In this regard, we introduce the notion of subgraph coverage patterns ( SCPs ). Given a GTD, a subgraph coverage pattern is a set of subgraphs subject to relative frequency, coverage and overlap constraints provided by the user. We propose the S ubgraph I D-based F lat T ransactional ( SIFT ) framework for the efficient extraction of SCPs from a given GTD. Our performance evaluation using three real datasets demonstrates that our proposed SIFT framework is indeed capable of efficiently extracting SCPs from GTD. Furthermore, we demonstrate the effectiveness of SIFT through a case study in computer-aided drug design.

show abstract

FERRARI: an efficient framework for visual exploratory subgraph search in graph databases

Cited by 5 publications

References 34 publications

GOMS: Large‐scale ontology management system using graph databases

GOMS: Large‐scale ontology management system using graph databases

FLAG: Towards Graph Query Autocompletion for Large Graphs

Mining subgraph coverage patterns from graph transactions

Contact Info

Product

Resources

About